We select object detection on COCO as the combined task which simultaneously performs both semantic understanding and geometric learning.
For object detection, a Mask-RCNN[1]} framework is adopted and trained with a 3\(\times \)  schedule (36 epochs). We utilize an AdamW [2]} optimizer with a learning rate of 6e-5/8e-5 for supervised/MIM model, a weight decay of 0.05, and a batch size of 32. We employ a large jittering augmentation (1024 \(\times \)  1024 resolution, scale range [0.1, 2.0]).
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